Based on current monthly figures, on average, German climate has gotten a bit warmer. The average temperature for January 2025 was recorded at around 2 degrees Celsius, compared to 1.5 degrees a year before. In the broader context of climate change, average monthly temperatures are indicative of where the national climate is headed and whether attempts to control global warming are successful. Summer and winter Average summer temperature in Germany fluctuated in recent years, generally between 18 to 19 degrees Celsius. The season remains generally warm, and while there may not be as many hot and sunny days as in other parts of Europe, heat waves have occurred. In fact, 2023 saw 11.5 days with a temperature of at least 30 degrees, though this was a decrease compared to the year before. Meanwhile, average winter temperatures also fluctuated, but were higher in recent years, rising over four degrees on average in 2024. Figures remained in the above zero range since 2011. Numbers therefore suggest that German winters are becoming warmer, even if individual regions experiencing colder sub-zero snaps or even more snowfall may disagree. Rain, rain, go away Average monthly precipitation varied depending on the season, though sometimes figures from different times of the year were comparable. In 2024, the average monthly precipitation was highest in May and September, although rainfalls might increase in October and November with the beginning of the cold season. In the past, torrential rains have led to catastrophic flooding in Germany, with one of the most devastating being the flood of July 2021. Germany is not immune to the weather changing between two extremes, e.g. very warm spring months mostly without rain, when rain might be wished for, and then increased precipitation in other months where dry weather might be better, for example during planting and harvest seasons. Climate change remains on the agenda in all its far-reaching ways.

A temperatura na Alemanha aumentou para 11,19 graus Celsius em 2024, em comparação com 10,89 graus Celsius em 2023. Esta página inclui um gráfico com dados históricos para a Temperatura Média da Alemanha.

In June 2025, the average temperature in Berlin was **** degrees Celsius. This was an increase compared to the June a year ago.

This statistic displays the average maximum monthly temperature in Germany over the past 20 years. It shows that over the past twenty years the month with the highest average maximum temperature has been July, with an average temperature of **** degrees Celsius. On average, January has been the coldest month.

In 2024, the average summer temperature in Germany was **** degrees Celsius. This was basically unchanged compared to the year before. While figures fluctuated during the given timeline, there were regular peaks, and in general, temperatures had grown noticeably since the 1960s. Not beating the heat German summers are getting hotter, and as desired as warm weather may be after months of winter (which, incidentally, also warms up year after year), this is another confirmation of global warming. Higher summer temperatures have various negative effects on both nature and humans. Recent years in Germany have seen a growing number of hot days with a temperature of at least 30 degrees, with **** recorded in 2023. However, this was a decrease compared to the year before. The number of deaths due to heat and sunlight had peaked in 2015. Rain or shine All the German states saw less sunshine hours in 2023 compared to the previous year. The sunniest states were Baden-Württemberg, Bavaria and Saarland. Meanwhile, summer precipitation in Germany varied greatly during the same timeline as presented in this graph, but 2022 was one of the dryest years yet.

In June 2025, the average precipitation amounted to 61 liters per square meter, an increase compared to the previous month. The rainiest state in Germany was Saarland.

In 2023/2024, the average winter temperature in Germany was *** degrees Celsius. That winter was part of a growing list of warmer winters in the country. Figures had increased noticeably compared to the 1960s. Warmer in the winter Everyone has a different perception of what actually makes a cold or warm winter, but the fact is that winter temperatures are, indeed, changing in Germany, and its 16 federal states are feeling it. Also in 2022/2023, Bremen and Hamburg in the north recorded the highest average figures at around 4 degrees each. The least warm states that year, so to speak, were Thuringia, Saxony, and Bavaria. The German National Meteorological Service (Deutscher Wetterdienst or DWD), a federal office, monitors the weather in Germany. Global warming Rising temperatures are a global concern, with climate change making itself known. While these developments may be influenced by natural events, human industrial activity has been another significant contributor for centuries now. Greenhouse gas emissions play a leading part in global warming. This leads to warmer seasons year-round and summer heat waves, as greenhouse gas emissions cause solar heat to remain in the Earth’s atmosphere. In fact, as of 2022, Germany recorded **** days with a temperature of at least 30 degrees Celcius, which was more than three times the increase compared to 2021.

Data sets of current German weather stations updated hourly or every twelve hours. Data sets, in German, include: * Daily mean values ??of temperature, updated hourly. Daily archive since 29.1.2008 * Daily maximum and minimum temperature, updated every 12 hours. Daily archive since 21.7.2008 * Monthly mean values ??of temperature and deviation, updated daily . * Rainfall in the last 12 hours and monthly total, updated every 12 hours . * Monthly totals of precipitation and relative to langj. means in%, updated every 12 hours. Monthly Archive since Feb. 2008 * Air pressure and pressure tendency, updated hourly.

Average temperature since 1881 in Germany, time series for regional funds for federal states and combinations of federal states.

This dataset contains outputs from two runs of a coupled atmosphere-ocean model at DKRZ in Hamburg. The runs were made in 1990 and they include a control run and an IPCC Scenario A run. We received 100 years of monthly 10-year climatologies of 2m temperature, precipitation, net surface solar radiation, and reflected surface solar radiation in GRIB0 format. We also received outputs from 100-year transient runs (control, IPCC Scenario A, and IPCC Scenario D). These included monthly means of 59 parameters at the surface and 15 isobaric levels. We were notified in May 1993 that there was a problem with the vertical interpolation in those runs, so the data are no longer in our public distribution, but they remain in our archive.

This data shows the average temperature in Germany 2024, by federal state. That year, the average temperature in the city-state Berlin was **** degrees Celsius.

The grids were derived from data from the DWD stations and qualitatively equivalent partner network stations in Germany, taking into account the altitude dependencies. pdf

Forecast: Average per Capita Monthly Mobile Data Use in Germany 2024 - 2028 Discover more data with ReportLinker!

This dataset contains monthly precipitation intensity maxima for 14 aggregation times at 132 stations in Germany that serve as the basis of the study Modeling seasonal variations of extreme rainfall on different time scales in Germany. Generation of the dataset: We use precipitation measurements at 132 stations in Germany that provide a temporal resolution of one minute. The majority (129) of these stations are operated by the German Meteorological Service (DWD) and were obtained via ftp://ftp-cdc.dwd.de/climate_environment/CDC/observations_germany/climate. The available time series at these stations range from 19 to 28 years. Additionally we use three stations operated by the Wupperverband (https://www.wupperverband.de) with time series of more than 43 years. The observations were accumulated to the following durations: (d \in 2^{\left\lbrace 0,1,2,..,13 \right\rbrace}\,\text{min} = \left\lbrace 1,2,4,...,8192 \right\rbrace\,\text{min}). Thus, resulting in 14 time series per station. Files and variables meta_data_seasonal_variations_IDF_germany.csv Meta information about the stations Variables: station name, station id (as provided by DWD), position (longitude, latitude), length of timeseries available Variable names: "StationName", "StationID", "Longitude", "Latitude", "NumberYears" monthly_maxima_seasonal_variations_IDF_germany.csv Monthly maxima for different durations (aggregation times) Variables: station id (as in meta file), year and month of observation, duration [h], observed monthly intensity maximum [mm/h] Variable names: "StationID", "Year", "Month", "Duration [h]", "MonthlyIntensityMaximum [mm/h]" Abstract of the study We model monthly precipitation maxima at 132 stations in Germany for a wide range of durations from one minute to about six days using a duration-dependent generalized extreme value (d-GEV) distribution with monthly varying parameters. This allows for the estimation of both monthly and annual intensity--duration--frequency (IDF) curves: (1) The monthly IDF curves are steeper in summer and exhibit higher intensities for short durations than in the rest of the year. Thus, everywhere in Germany short convective extreme events occur very likely in summer. In contrast, extreme events with a duration of several hours up to about one day are more likely to occur within a longer period or even spread throughout the whole year, depending on the station. There are major differences within Germany with respect to the months in which long-lasting stratiform extreme events are more likely to occur. At some stations the IDF curves (for a given quantile) for different months intersect. The meteorological interpretation of this intersection is that the season at which a certain extreme event is most likely to occur shifts from summer towards autumn or winter for longer durations. (2) We compare the annual IDF curves resulting from the monthly model with those estimated conventionally, that is, based on modeling annual maxima. We find that adding information in the form of smooth variations during the year leads to a considerable reduction of uncertainties. We additionally observe that at some stations, the annual IDF curves obtained by modeling monthly maxima deviate from the assumption of scale invariance, resulting in a flattening in the slope of the IDF curves for long durations. Acknowledgements The authors would like to thank the Wupperverband, and in particular Marc Scheibel, as well as the Climate Data Center of the DWD, for providing and maintaining the precipitation time series.

The historical monthly degree days are calculated from publicly available station data from the DWD Climate Data Center (CDC). The monthly degree days according to VDI 3807 are the sums of the degree days over a calendar month. The degree days refer to a room temperature of 20 degrees Celsius. Degree days are calculated as the temperature difference between room temperature and the daily mean temperature (degrees Celsius). Only the days are counted when the daily average outside temperature is less than 15 degrees Celsius (heating day).
Further information: https://opendata.dwd.de/climate_environment/CDC/derived_germany/techn/monthly/heating_degreedays /hdd_3807/historical/BESCHREIBUNG_derivgermany_techn_monthly_heating_degreedays_hdd_3807_historical_de.pdf

These are data for running the ABSOLUT R programs published separately on Zenodo: 10.5281/zenodo.4468608.

The data published here consist of:

absolutcontrol.dat – text file (UTF-8) with case-specific settings for program execution, may be edited by the user

crop-areas.csv – CSV table of crop areas in hectares for different crops in German administrative areas. Modified from data originally provided by the statistical offices of Germany (© Statistische Ämter des Bundes und der Länder, Deutschland, 2020) and re-distributed here in this modified form also under the terms of the Data licence Germany – attribution – version 2.0, see https://www.govdata.de/dl-de/by-2-0

districtweather.zip – zipped directories DistrictWeather and DistrictFeatures. DistrictWeather contains 401 ASCII DAT files with monthly weather variables, one per district. These have been generated using rasterized weather data from the German meteorological service (Deutscher Wetterdienst, DWD), an official digital map of administrative boundaries provided by the German Federal Agency for Cartography and Geodesy, and CLC-2012 land use data provided by Copernicus. DistrictFeatures is an empty directory to be used by the corresponding program.

climatescenarios.zip - zipped directories ClimateScenarios and ClimateScenarioFeatures. ClimateScenarios contains three subdirectories with example climate scenario realisations in the same format as DistrictWeather. ClimateScenarioFeatures is an empty directory to be used by the corresponding program.

yield-indat.csv – CSV table of crop yields in dt/ha for different crops in German administrative areas, annual values for the years 1999–2020. Modified from data originally provided by the statistical offices of Germany (© Statistische Ämter des Bundes und der Länder, Deutschland, 2021) and re-distributed here in this modified form also under the terms of the Data licence Germany – attribution – version 2.0, see https://www.govdata.de/dl-de/by-2-0

This dataset comprises synthetic weather data generated for historical (“control” present, 1985-2014) and two future periods (near future: 2031-2060 (period1) and far future: 2071-2100 (period2)) across a domain encompassing Germany and its neighboring riparian countries. The dataset was produced through the following key steps: (1) Classifying Weather Circulation Patterns for the Observed/Present Period: Weather circulation patterns (CPs) were classified for a European domain (35°N – 70°N, 15°W – 30°E), and regional average temperatures at 2 m height (t2m) were calculated for the German domain (45.125°N – 55.125°N, 5.125°E – 19.125°E). This classification used mean sea level pressure (psl) and mean temperature (tas) data from the ERA5 dataset provided by the European Centre for Medium-Range Weather Forecasts (ECMWF) (Hersbach et al., 2020). (2) Training Non-Stationary Climate-Informed Weather Generator (nsRWG): The nsRWG (Nguyen et al., 2024), conditioned on the classified CPs and using tas as a covariate, was set up and trained for the German domain using the E-OBS dataset, version 25.0e (Cornes et al., 2018). This training dataset includes 540 grid cells of mean daily temperature and precipitation totals for the period 1950–2021, with a spatial resolution of 0.5° x 0.5°. (3) Generating Data for the Present Period: Long-term synthetic data for the present period is generated using the trained nsRWG. (4) Assigning Circulation Patterns for Future Periods: The classified CPs from the present period were assumed to remain stable in the future. These CPs were assigned to future periods based on mean sea level pressure data from nine selected general circulation models (GCMs) from CMIP6 (Eyring et al., 2020) for the two future periods and two shared socio-economic pathways: SSP245 and SSP585 (IPCC, 2023). In total, CPs were derived for 36 scenarios, and regional average temperatures were also computed. (5) Downscaling Data for Future Scenarios: The nsRWG was used to statistically downscale long-term synthetic weather data for all 36 future scenarios. (6) Final dataset: The dataset includes synthetic weather data generated for the present period (Step 3) and future scenarios (Step 5). This dataset is expected to offer a key benefit for hydrological impact studies by providing long-term (thousands of years) consistent synthetic weather data, which is indispensable for the robust estimation of probability changes of hydrologic extremes such as floods.

In 2024, the average autumn temperature in Germany was 10.5 degrees Celsius. This was a decrease from the previous year, when the average temperature in autumn was around 11.5 degrees Celsius. This statistic shows the average autumn temperature in Germany from 1960 to 2024.

Forecast: Average Monthly Fixed Broadband Data per User in Germany 2022 - 2026 Discover more data with ReportLinker!

Forecast: Average Monthly Mobile Data Usage per SIM-card in Germany 2022 - 2026 Discover more data with ReportLinker!